Frames the problem.
An LLM turns goals, constraints, and evidence into formal terms a solver can read.
We solve complex decisions from messy goals, constraints, and evidence — into answers that are derivable, consistent, and verifiable. A system that gets smarter with every problem it sees.
An LLM turns goals, constraints, and evidence into formal terms a solver can read.
An algorithm solves the formulation — calling a dedicated solver where one fits, or running our own where it doesn't. The answer is one you can derive, reproduce, and verify.
A library remembers every solved problem, so the next decision starts smarter.
The decision-maker keeps the judgment.
The system handles the modeling.
We translate the goal, the constraints, and the evidence into a formal optimization program — variables, objectives, constraints a solver can read.
An algorithm returns the answer, or proves the problem can't be satisfied as stated.
The answer comes with its reasoning, assumptions, and where judgment is still needed — reviewable, not a black box.
What we learned formulating this problem becomes a structured insight in a library. The next problem starts smarter.
Decision problems where capacity, evidence, and timing move at once — and someone still has to ship a plan.
Patient flow, staffing, and care decisions that compound.
Demand, margin, inventory, and customer trust in the same plan.
Hypotheses, diagnostics, and machine limits in one campaign.
Demand, vehicles, routing, and the promises you've already made.
Gates, disruptions, turnaround — explainable to the operator.
Capacity, demand, and service targets competing for the same hour.
Every plan shows its formulation, its solution, and the assumptions behind it. Decisions you can defend. What works gets stored — not as data, but as a modeling insight the next decision can use.
Builders, researchers, and product engineers who want AI to be useful where decisions carry real weight.